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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- tweet_eval |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: tweet_eval-sentiment-finetuned |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: sentiment |
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type: sentiment |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.70 |
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- name: f1 |
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type: f1 |
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value: 0.70 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# tweet_eval-sentiment-finetuned |
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This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the tweet_eval dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8369 |
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- Accuracy: 0.7305 |
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- F1: 0.7297 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 8e-05 |
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- train_batch_size: 128 |
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- eval_batch_size: 256 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 4 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| 0.7269 | 1.0 | 357 | 0.6057 | 0.733 | 0.7323 | |
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| 0.522 | 2.0 | 714 | 0.6115 | 0.7415 | 0.7416 | |
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| 0.359 | 3.0 | 1071 | 0.6970 | 0.744 | 0.7445 | |
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| 0.2386 | 4.0 | 1428 | 0.8369 | 0.7305 | 0.7297 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.9.1 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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